GraphRAG vs. Vector RAG: Where the Accuracy Gap Actually Comes From

GraphRAG-Bench, cited as an ICLR 2026 evaluation, reports that FalkorDB’s GraphRAG SDK scored 71.48 overall versus 55.39 for a reranked vector RAG baseline using the same generation models and judges, with especially large advantages in fact retrieval and contextual summarization. The comparison attributes GraphRAG’s performance to canonical entity storage, explicit relationship traversal, and structured context assembly, which can improve precision for entity-specific attributes, multi-entity queries, provenance, and comprehensive summaries. Vector RAG remains more competitive for complex reasoning and creative or exploratory generation, where retrieval precision is less decisive and the language model’s reasoning capabilities become a larger constraint. The proposed practical approach is often hybrid: use vector search for broad semantic access to unstructured content and graph retrieval as the primary method where applications require accurate entity lookups, relationship-based reasoning, traceability, or entity-level access controls.

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